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Resources to learn how to manage corpus with Python.


Installation – for those who like not to ask too many questions

  1. Download Miniconda, a small version of Anaconda with conda, Python, and some useful packages:

  2. Get a clone of this repository:

$ git clone
$ cd compuling
  1. Run JupyterLab in its own environment:
$ conda env create -f environment.yml
$ conda activate compuling
$ jupyter-lab

Installation – for those who want to have fine control over what they install

Python 3 installation

  1. First, check that Python is installed:
$ which python

If not, donwload the latest version:

  1. Check the version of your Python distribution (at least 3.7):
$ python -V

If your version is older than 3.7, you may have a specific python3.7 binary:

$ which python3.7

If so, note the path and link it with the python command:

$ ln -s PYTHON3.7_PATH python
  1. Be sure to have the latest version of pip, the Python package manager:
$ python -m pip install --user --upgrade pip

Installing a virtual environment

  1. As venv is already included in the Python standard library, you just need to install a new virtual environment:
$ python -m venv tal-ml
  1. Activate your environment:
$ source compuling/bin/activate

To leave you environment, just run the command deactivate.

Dependencies installation

With pip, install all the requested packages into your environment:

$ python -m pip install -r requirements.txt

Run JupyterLab

Simply run JupyterLab with a clone of this repository:

$ git clone
$ cd python-M1TAL
$ jupyter-lab